Introduction
In this T.U. students will learn to understand, manage and analyze high dimensional "omics" data, such as gene polymorphism or gene expression (transcriptomics) data. Topics range from principles of experimental clinical trials with high dimensional omics data in small sample sizes to methods of gene-wise association analyses (GWAS) in large observational studies. By the use of case studies and hands-on computer programming to analyze real data during this unit, the students will acquire knowledge in dimension reduction methods, multiple testing procedures and longitudinal data modelling. Together with annotation visualisation techniques, this course will also also teach how to interpret and communicate results of omics analyses.
Coordinators: Laura Richert & Rodolphe Thiébaut
Type of assessment: continuous assessment in teamwork (students will be asked to perform data analyses based on examples seen in class) plus written exam. A catch-up session is envisaged for those who fail the first session of the written exam.
- Enseignant: Boris Hejblum
- Enseignant: Florence Lamarque
- Enseignant: Delphine Manceau
- Enseignant: Ilaria Montagni
- Enseignant: Carine Prevot
- Enseignant: Laura Richert
- Enseignant: Anais Rouanet